11 application of the ahp-topsis method to determine the

Jurnal_Pekommas_Vol._6_No._1,_April_2021:_01_–_11

.

doi: 10.30818/jpkm.2021.2060101

Application of the AHP-TOPSIS Method to Determine the Feasibility of

Fund Loans

Penerapan Metode AHP TOPSIS untuk Menentukan Kelayakan Pinjaman

Dana

Juniar Hutagalung

STMIK Triguna Dharma

Jl. Pintu Air I/Jend. AH. Nasution No. 73 Medan Johor

[email protected]

Diterima : 20 April 2020 || Revisi : 22 Februari 2021 || Disetujui: 24 Februari 2021

Abstract – This study aims to produce a decision support system (DSS) for the feasibility of providing loan

funds as a tool and recommendation for cooperatives with several criteria as the basis for decision making,

namely: business ownership status, ability, character, collateral, income, and salary. The system

implementation uses the Visual Studio 2010 and Microsoft Access 2010 programming languages. This

application is designed for effective and efficient decision-making. This program uses two methods, namely

AHP for determining the weight of the criteria, and TOPSIS for determining to rank. This combination is

designed for high-accuracy applications. The results of the pairwise comparison matrix calculation show that

the weights obtained are acceptable and consistent. This application generates alternative customer data in

order from the highest preference value (very feasible to get a loan) to the lowest (not feasible).

Keywords: AHP, Cooperative, DSS, Loans, TOPSIS

Abstrak - Penelitian ini bertujuan untuk menghasilkan sistem pendukung keputusan (SPK) kelayakan

pemberian pinjaman dana sebagai alat bantu dan rekomendasi bagi pihak koperasi dengan beberapa kriteria

menjadi dasar pengambilan keputusan, yaitu: status kepemilikan usaha, kemampuan, karakter, agunan,

penghasilan, dan gaji. Implementasi sistem menggunakan bahasa pemrograman Visual Studio 2010 dan

Microsoft Access 2010. Aplikasi ini dirancang agar pengambilan keputusan efektif dan efesien. Program ini

menggunakan dua metode yaitu AHP untuk penentuan bobot dari kriteria-kriteria, dan TOPSIS untuk

penentuan perangkingan. Pengombinasian ini dirancang agar aplikasi memiliki akurasi tinggi. Hasil

perhitungan matriks perbandingan berpasangan memiliki menunjukkan bahwa bobot yang diperoleh dapat

diterima dan konsisten. Aplikasi ini menghasilkan data alternatif nasabah secara terurut mulai dari nilai

preferensi yang paling tinggi (sangat layak mendapatkan pinjaman dana) hingga terendah (tidak layak)..

Kata Kunci: AHP, Koperasi, DSS, Pinjaman, TOPSIS

INTRODUCTION

Savings and loan cooperatives are a type of

cooperatives in Indonesia that have activities, in

essence, providing services in terms of savings and

loan funds in the form of money for members of the

cooperative as well as the community. XYZ

cooperative is a type of active savings and loan

cooperative, which utilizes member savings and then

distributes to members/customers again in the form of

loans to set up a business or in meeting the cost of

living of its customers. Loan types are based on short,

medium, and long-term repayments. However, this

often experiences risks and obstacles, including arrears

and late payments, and not making advance payments

for various reasons from customers. Therefore,

cooperatives need a policy in granting loans by setting

standards to accept or reject these risks, namely by

determining which loans are appropriate according to

the criteria needed, including business ownership

status, ability to repay loans, customer character,

collateral, customer's income, and salary.

Based on the background, that the XYZ cooperative

in the process of selecting proper customers and getting

loans is still inaccurate so that it requires computer-

based applications and appropriate methods to select

several criteria in determining whether or not

customers are eligible for loans. Cooperative leaders

need a computer-based application and methods that

are more precise, accurate, fast, and relevant for the

customer data collection process to be more directed.

The cooperative leader as the decision-maker must

Application of the AHP-TOPSIS Method ... (Juniar Hutagalung)

2

have an effective and efficient decision support system

to decide whether or not the customer is given a loan

within the time frame and conditions provided by the

cooperative.

In this study, researchers designed and built a

decision support system for lending at the XYZ

cooperatives by combining the Analytic Hierarchy

Process (AHP) method and Technique for Others

Reference by Similarity to Ideal Solution (TOPSIS),

which is expected to help in the selection of

members/customers which is more appropriate and

feasible for receiving loans from the cooperative. The

lending needs to take into account the risks that will

occur to members because it affects the financial

condition of the cooperative as well. For this reason, a

computer-based decision support system is needed that

can produce output in the form of information quickly

related to lending criteria, whether or not a member

receives a loan. The existence of a decision support

system can provide information based on analysis so

that it is more efficient in decision making in an

organization (Budiharjo, Windarto, & Muhammad,

2017).

The AHP method is more effective for the selection

of railroad technical facilities at PT. KAI Diver I

Medan (Fifin, 2017). To make it easier to determine

policies and strategies for the right solution to reverse

logistical barriers, a decision support system is

implemented by combining the AHP and TOPSIS

methods. AHP for determining criteria weights and

TOPSIS are used for alternative ranking stages so that

decisions taken are more effective and efficient

(Pornwasin & Tossapol, 2018).

The AHP-TOPSIS method to determine the priority

of road improvement, the level of accuracy obtained is

not too high due to the implementation of road

improvement there are still personal interests in it so

that it is not well-targeted in handling road repair

(Firdaus, Muhammad & Nurudin, 2018). To produce a

more objective ranking and appropriate

recommendations for selecting the best prospective

employees the AHP-TOPSIS method was applied

(Santika & Handika, 2019). The AHP-TOPSIS method

of accuracy in filling the pairwise comparison matrix

will give more accurate results for the recommendation

of PC package selection (Bhima, Rekyan. & Nurul,

2018). The describes a decision support system

functions to help a manager in terms of decision

making in a structured and half-structure so that it is

right on target to apply analytical models and existing

data (Ahmadi, Sarjon & Jufriadif, 2018). The

determination of beneficiaries of the Family of Hope

Program uses the AHP and TOPSIS methods with

twelve criteria being compared to find the weight

values for each of these criteria. System Usability Scale

(SUS) test results in this study obtained an average of

82.5 which indicates that this system belongs to the

acceptable category (Hasanah, 2016).

The purpose of this study as an alternative is to assist

the cooperative in determining the decision to choose a

suitable customer to get a loan/credit following the

criteria. Conduct an assessment of each criterion for the

selection of members/customers who are eligible and

make decision support to get members/customers who

meet the criteria quickly and accurately.

RESEARCH METHODOLOGY

A. Decision Support System (DSS)

AHP and GIS are a combination of appropriate

methods to determine data spatially and can evaluate

coal deposits as an alternative power generation

solution that is widely applied to increase the economic

potential of mineral reserves and evaluate coal deposits

because many factors affect the energy sector. The

AHP method determines the weights of each criterion

while the ArcGIS application is used to map and

evaluate the sustainable exploitation of mineral

deposits in the framework and other geospatial data

(Nikolas, et al, 2019).

A comprehensive and innovative evaluation method

is used to analyze the static voltage stability in the

electric power system by utilizing EW-AHP and

Fuzzy-TOPSIS. Fuzzy-TOPSIS is used to determine

the bus voltage rating of the power system as the final

result, taking into account the system's functionality

and proportionality. The combination of these two

methods is an effective approach to determine the

weakest buses in the electric power system (Jiahui, et

al, 2019). The decision support system is a process or

action to achieve one goal or several goals (Christine &

Yeremia, 2018). The decision support system is

interactive that can present information, modeling, and

manipulation of data that is useful to facilitate decision-

makers in making the right decision in semi-structured

and unstructured situations (Frieyadie & Surya, 2018).

The decision support system can solve unstructured

problems by choosing several alternatives and no one

knows for sure how the decision is taken but it produces

Jurnal Pekommas, Vol. 6 No. 1, April 2021: 01 - 11

3

output in the form of flexible, interactive, and adaptive

information (Oktopanda. 2017).

B. Analytic Hierarchy Process (AHP)

The AHP method is used in decision making to

deconstruct the complexity site planning and resource

management and evaluates the value as a policy

recommendation for use and rebuilding because it is a

valuable source of human cultural heritage (Ma, Li, and

Chan, 2018). AHP makes it easy to solve complex

problems by arranging criteria hierarchically, so we can

determine the weight or priority (Rahayu, Krisnanik, &

Hananto, 2019). AHP and FAHP methods are used as

recommendations and make it easy for companies to

make decisions to solve multi-critical problems, to

reduce the risk of loss for bus body manufacturing

companies because many parts must be produced in a

short time. Risks can occur when different companies

work together to make the same product and share

profits. With the application of the AHP and FAHP

methods, the results are more appropriate because they

can increase productivity and reduce the effect of

capacity, time, and cost of capacity to make parts of a

bus body (Suthep & Puntiva, 2019).

The results of the validation test conducted by the

AHP method obtained the final value: 3.8 and

concluded the system is feasible to be used to improve

teacher performance (Sindhu, 2018). The decision

support system research to select priority areas for

intervention in family planning activities was built

based on the website using the AHP-SMART method

(Karmila, Tursina & Muhammad, 2019). E-commerce

businesses using AHP need to consider websites and

trademarks. The investment factor in the brand is the

most important thing for forming a trademark (Tayfun,

2017). AHP method is a method for making decisions

by deciding without trial and before finding a solution

to the existing constraints then systematically arrange

the way it works (Harli, 2016).

Figure 1 AHP Hierarchy Structure

Below are some AHP principles that must be

understood, namely (Rizqi, Akbar, Fitrian, &

Maseleno, 2018):

1. Decomposition (create a hierarchy)

A complete and complicated system is easy to

understand if it has been broken down into the smallest

parts.

2. Comparative judgment

Make a pairwise comparison, to obtain the scale of

importance of each criterion against the other criteria.

Table 1 Pairwise Comparison Rating Scale The intensity of

interest

Definition

1 3

5

7

9

2, 4, 6, 8

Inverse

Both elements are equally important Elements that one a little more important than the

other elements

Elements which one is more important than any other element

One element more important than any other element

One essential element than other elements Values between two values adjacent

consideration

If the first activity in the appeal activities got the numbers j, then j has its inverse value Compared

with i

3. Synthesis of priority

4. Logical Consistency

The steps for implementing the AHP method are as

follows:

1. Define the problem and determine the solution,

then arrange the hierarchy.

2. Determination of element priority.

a. Determine the pair ratio.

b. Make a pairwise comparison matrix.

3. Synthesis, things are done at this stage are:

a. In the matrix add up the values for each column.

b. The value of the column is divided by the total

column to produce a normalized matrix, the formula:

𝑗=1 𝑛 𝑎𝑖𝑗 = 1 ........................... (1)

Where:

a = Pairwise comparison matrix

i = matrix row a

j = matrix column a

c. The value of each row is added up and divided

by the number of elements to get the average value, the

formula:

𝑊𝑖 = 1

𝑛 𝑗=1

𝑛 𝑎𝑖𝑗 ................................ (2)

Where:

n = Number of criteria

Wi = Average of line I

4. Measuring Consistency, the steps are:

a. The value in the first column and the relative

priority of the first element are multiplied, and so on.


4

b. Each row is added together.

c. From the sum of the rows, the results are

divided among the relative priority elements

concerned.

d. The result of the division in point (c) is summed

by the number of elements, the result is called lambda

(λ) max.

5. Determine the Consistency Index (CI), the

formula:

𝐶𝐼 = (max −𝑛) (𝑛 − 1) ⁄ .......... (3)

6. Calculating Consistency Ratio (CR), the formula:

𝐶𝑅 = 𝐶𝐼 𝑅𝐼 ⁄ ................................. (4)

7. Check the consistency of the hierarchy. If the

value is > 10%, then it needs to be improved, if the

Consistency Ratio <0.1 then the calculation results are

correct.

The value of 0 ≤ ratios ≤ 0.1 is called consistent then

the calculation is justified. Below can be used the

random index Table 2.

Table 2 Random Index Values (RI) n RI

1 0 2 0

3 0.58

4 0.90 5 1.12

6 1.24

7 1.32 8 1.41

9 1.45

10 1.49 11 1.51

12 1.48

13 1.56 14 1.57

15 1.59

C. Technique Others Preference by Similarity to

Ideal Solution (TOPSIS).

One of the multi-criteria decision support systems is

the TOPSIS method (Nofriansyah, 2014). The TOPSIS

method is an optimization technique used to identify

the best combination of parameters optimally for multi-

response characteristics and Analysis of Variance

(ANOVA) is used to determine the most significant

parameters in the overall Multi-object function and it

can be concluded that the laser power is very large on

the overall Multi-object function (Sampreet, et al,

2019). High energy use and severe levels of air

pollution caused by winter warming have worsened in

China in recent years. The policy of replacing coal-

fired boilers with gas fuel for central heating is very

important for development in China. To overcome this,

the TOPSIS method is used as a recommendation for

the government to make the right decisions in

improving environmental quality through energy

savings and reducing emissions (Jing, Yaoqi &

Xiaojuan, 2019).

The decision support system developed using the

TOPSIS Method can assist in making decisions in

determining the best employees. Based on calculations

using the TOPSIS method, it was found that V5

(Employee 5) was the best employee because it had the

best value (Hertyana, 2018). The application of the

TOPSIS method is designed to solve measurable

problems for the financial feasibility decision support

systems so that they are more objective in the

assessment results taken (Mubarok, et al, 2019). The

system produced using the TOPSIS method can

recommend the selection of priority areas of stunting

treatment experienced by toddlers from the largest

preference value to the smallest preference value

(Mahmud, Tursina & Yulianti, 2019).

In general, the TOPSIS method procedure follows

steps (Dwi & Rostika, 2017):

Determine the normalized decision matrix.

Calculate the weighted normalized decision matrix.

Calculate the ideal and negative ideal solution

matrices.

Calculate the distance between the values of each

alternative with the positive and negative ideal solution

matrices.

Calculate the preference value for each alternative.

The systematic steps of the TOPSIS method are as

follows (Munawir, 2018) :

a. Starting to make a decision matrix that is

evaluating alternative m in a decision matrix X

based on n criteria, used with the equation 5.

b. Determine a normalized decision matrix, it can

be used with the equation 6.

c. Determine the weighted normalized decision

matrix, used with the equation 7.

𝑥1 𝑥2 𝑥3 … 𝑥𝑛

𝑎1 𝑥11 𝑥12 𝑥31 … 𝑥𝑛1

𝑎2 𝑥12 𝑥22 𝑥32 … 𝑥𝑛2

𝑋 = 𝑎3 𝑥13 𝑥32 𝑥33 … 𝑥𝑛3

⋮ ⋮ ⋮ ⋮ ⋮

𝑎𝑚 𝑥𝑚1 𝑥𝑚2 𝑥𝑚3 … 𝑥𝑚𝑛 ..... (5)

.......................... (6)

𝑉𝑖𝑗 = 𝑊𝑗 ∗ 𝑟𝑖𝑗 ............................................ (7)

;

1

2 =

= m

i ij

ij ij

x

x r


5

d. Determine the ideal (A +) and negative (A-)

ideal solution matrices, used with the equation

8 dan 9.

𝐴+ = (𝑦 1+, 𝑦 2

+, … , 𝑦 𝑛+) ............................... (8)

𝐴− = (𝑦 1−, 𝑦 2

−, … , 𝑦 𝑛−) .................................... (9)

Where:

𝑦 𝑗+ = maxi yij ; if j atribut benefit

mini yij ; if j atribut cost

𝑦 𝑗− = mini yij ; if j atribut benefit

maxi yij ; if j atribut cost

e. Calculate the distance of the positive (D +) and

negative (D-) ideal solutions. D + is the

alternative distance from the positive ideal

solution, used with the following equation:

𝐷𝑖+ = √ 𝑗=1

𝑛 (𝑦𝑗

+− 𝑦𝑖𝑗)2 .................. (10)

D- is the alternative distance from the negative

ideal solution, used with the following

equation:

𝐷𝑖− = √ 𝑗=1

𝑛 (𝑦𝑖𝑗 − 𝑦𝑗

+)2 .... (11)

f. Calculation of preference values (Vi) for each

alternative. used with the following equation:

𝑉𝑖 = 𝐷𝑖

−

𝐷𝑖− + 𝐷𝑖

+ ........................... (12)

g. Ranking alternatives by sorting alternatives

from the largest Vi value to the smallest value.

The best solution if the alternative is the best

Vi value.

Collecting data by observing objects by holding

direct questions and answers with the cooperative. The

data needed are criteria data, alternative data, and

weight data obtained from the questionnaire results.

The technique of taking respondents was done using

the purposive sampling method, namely selecting

respondents deliberately related to the research topic.

This technique is used by considering that the

respondent has the competence in assessing customers

who represent the company and has the authority to

provide the information and data needed in the study.

Respondents in this study were two people, namely

supervisors and managers of the cooperative.

AHP method has a weakness in the principle of

pairwise comparison, takes time, and the consistency

index is fulfilled. These deficiencies make it difficult

for solutions to require many choices. The TOPSIS

method can be used to make practical decisions. The

weights that have been obtained from the AHP

calculation are used as input in the calculation of the

TOPSIS method. Therefore, a model is needed to

make it easier for the cooperative to determine

potential customers who are eligible for a loan. The

AHP method is a decision support system method that

can give weight to the criteria and test its consistency.

The criteria used in determining the eligibility of a

loan are Business Ownership Status, Capability,

Character, Collateral, Income, and Salary. The weight

obtained from the AHP method becomes the input

value in the TOPSIS method in sorting the

alternatives to be selected. The result of the sorting is

calculated for the level of accuracy. The TOPSIS

method is a method that has the concept of choosing

the closest alternative distance with a positive ideal

solution and having the farthest distance with a

negative ideal solution.

The focus of this research is to build SPK to

determine the feasibility of applying for a loan using

the AHP-TOPSIS method to speed up the process and

produce an optimal decision value. The research

framework begins with identifying problems.

Identification of the problem with determining priority

weights does not yet exist on the criteria selected to

rank to determine the feasibility of submitting a loan

then collecting data. The data used in this study are

primary data through the process of observation and the

relevant sources directly and secondary obtained from

the theory or related material under study. The

technique used to collect data is done by a literature

study, interviews who know the criteria and

observations.

Figure 2 Use Case Diagram


6

The perform analysis criteria and implementation of

AHP-TOPSIS to obtain an alternative ranking. AHP

method is used for the weighting process then the

application of the TOPSIS method is carried out to

perform an alternative ranking process. The design of

the use case diagram is shown in Figure 2.

In Figure 3 the following are the steps - the

calculation of AHP-TOPSIS. Customer data input is

performed as a determinant in the normalized matrix.

Furthermore, weights are obtained to determine the

ideal and negative ideal solution matrix. From the

distance between the values of the solution matrix, the

preferences can be determined for each alternative and

then ranking. The best solution is an alternative with

the largest Vi value.

RESULT AND DISCUSSION

3.1. Calculation Analysis of AHP Method

The AHP method itself is inseparable from its

shortcomings, the AHP method is not effective when it

is used in cases with many criteria and alternatives,

therefore another method is needed to be combined

with the AHP method in the order to obtain more

effective results. The AHP method has the advantage

of being based on a pair of comparison matrices and

conducting a consistency analysis.

The AHP method is used in determining the

weighting of criteria because the AHP method relies on

the thinking of an expert or expert to determine the

assessment of each criterion and alternative used, the

element of objectivity will still exist even though the

assessment is carried out by an expert because in the

AHP method there is a consistency ratio assessment to

assess whether an expert's assessment can be accepted

with a consistent ratio value, it is still acceptable if it is

used in weighting each criterion, but it is very risky

when used to assess an alternative, however, the

element of subjectivity will be felt if the AHP method

is used to select or prioritizing the best alternative.

Therefore, another method is needed to be combined

with the AHP method, namely the TOPSIS method.

The TOPSIS method was chosen because the TOPSIS

method could complete practical decision-making.

After all, its concept simple and easy to understand, the

computation is efficient and can measure the relative

performance of the decision alternatives. In addition,

the TOPSIS method can handle alternative differences

even though the differences are quite small, in the

TOPSIS method itself there is a Cost and Benefit rule

to determine the rules for each criterion, with these

advantages the combination of AHP and TOPSIS

methods can be applied to decision support systems to

produce decisions. effective, efficient, and objective.

Figure 3. AHP-TOPSIS Algorithm Flowchart

In this study, the determination of the criteria

weights utilizes the AHP method, while the ranking

stage is done using the TOPSIS method. The criteria in

determining the eligibility of applying for a loan can be

seen in Table 3. Business Ownership Status (C1),

Capability (C2), Character (C3), Collateral (C4),

Income (C5), and Salary (C6).

After determining the criteria, then the value or

weight of each criterion is made for each alternative,

the next step is to analyze the system being made, the

results or system output is information about the

value of alternative customers that are feasible or not

to get a loan and the criteria they have to serve as

End

Customer

data input

Matrix

normalization

Calculate the matrix

normalized weighted

Calculating positive

and negative ideal

solutions

Calculate distance

between alternatives

Calculating

preferences

every alternative

Alternate ranking

Input from

customer master

data and data entry

The priority weight

of the AHP

algorithm

Calculation results

are used for

feasibility

assessment

Implementation

TOPSIS algorithm

Alternative results

with the largest Vi

value are the best

solutions

Start


7

recommendations for cooperatives so that they can

easily and quickly make decisions for customers who

are eligible for loans.

Figure 4 Hierarchical Structure for Applying for a Loan

The number data contained in Table 4 is obtained

from Table 1 which is a comparison scale table of

criteria values. Table 6 is the decimal value of the

numeric data in Table 3.

Table 3 Pairwise Comparison Matrix Criteria C1 C2 C3 C4 C5 C6

C1

C2

C3

C4

C5

C6

1

2

1/3

1/4

1/5

1/5

1/2

1

1/3

1/3

1/3

1/3

3

3

1

1/2

1/3

1/3

4

3

2

1

1/2

1/1

5

3

3

2

1

2

5

3

3

1

1/2

1

Table 4 Calculation Results in Decimal Form

Criteria C1 C2 C3 C4 C5 C6

C1

C2

C3

C4

C5

C6

1

2

0.33

0.25

0.20

0.20

0.50

1

0.33

0.33

0.33

0.33

3

3

1

0.50

0.33

0.33

4

3

2

1

0.50

1

5

3

3

2

1

2

5

3

3

1

0.50

1

Total 3.98 2.83 8.17 11.50 16.00 13.50

Table 5 Normalized Pairwise Comparison Matrices Criteria C1 C2 C3 C4 C5 C6

C1

C2

C3

C4

C5

C6

0.25

0.50

0.08

0.06

0.05

0.05

0.18

0.35

0.12

0.12

0.12

0.12

0.37

0.37

0.12

0.06

0.04

0.04

0.35

0.26

0.17

0.09

0.04

0.09

0.31

0.19

0.19

0.13

0.06

0.13

0.37

0.22

0.22

0.07

0.04

0.07

Total 1 1 1 1 1 1

After the number of columns is determined, the next

step of the numbers in Table 6 is divided by the number

of columns, resulting in a normalized matrix. In column

C1, divide row C1 by the number of columns

C1=1/3.98=0.25. And so on until C6, the results are in

Table 5.

Next look for the priority weight scale, through the

calculation of the average row in Table 5, for example

the following calculation: C1 = (0.25 + 0.18 + 0.37 +

0.35 + 0.31 + 0.37) / 6 = 0.30. Calculations are carried

out until C6, so we get the priority in Table 6 below.

Table 6 Priority Weight Scale Criteria C1 C2 C3 C4 C5 C6 No.

Rows

Priority

C1

C2

C3

C4

C5

C6

0.25

0.50

0.08

0.06

0.05

0.05

0.18

0.35

0.12

0.12

0.12

0.12

0.37

0.37

0.12

0.06

0.04

0.04

0.35

0.26

0.17

0.09

0.04

0.09

0.31

0.19

0.19

0.13

0.06

0.13

0.37

0.22

0.22

0.07

0.04

0.07

1.83

1.89

0.91

0.53

0.35

0.49

0.30

0.32

0.15

0.09

0.06

0.08

Total 1 1 1 1 1 1 6 1

The consistency matrix is shown in Table 7,

examples of calculations are: C1 = (1 * 0.30) + (0.50 *

0.32) + (3 * 0.15) + (4 * 0.09) + (5 * 0.06) + (5 * 0.08)

= 1, 97.

The calculation is done until C6, so we get the

consistency matrix table in Table 7 below.

Table 7 Consistency Matrix Criteria C1 C2 C3 C4 C5 C6 Priority Product

C1 C2

C3

C4 C5

C6

1 2

0.33

0.25 0.20

0.20

0.50 1

0.33

0.33 0.33

0.33

3 3

1

0.50 0.33

0.33

4 3

2

1 0.50

1

5 3

3

2 1

2

5 3

3

1 0.50

1

0.30 0.32

0.15

0.09 0.06

0.08

1.97 1.91

0.96

0.54 0.36

0.50

Next, determine the consistency of the vector. This

is done by dividing the number of consistency matrices

in Table 7 with the weighted values obtained, namely

Table 6. For example 1.97 / 0.30 = 6.57, so the vector

consistency is as Table 8.

Table 8 Vector Consistency Criteria Vector Priority Product Vector Consistency

C1

C2

C3

C4

C5

C6

0.30

0.32

0.15

0.09

0.06

0.08

1.97

1.91

0.96

0.54

0.36

0.50

6.57

5.97

6.40

6.00

6.00

6.25

Total 37.19

Principle of Consistency: From Table 10 we can

calculate the value of lambda (λ) max, CI and CR with

the formula in equations (1), (2), and (3) whose results

are:

a. λ max = Number of Vector Consistency /

Number of Criteria

λ max = 37.19 / 6 = 6.20

b. CI = (λmax-n) / (n-1)

= (6.20-6) / (6-1) = 0.04

c. CR = CI / IR (Random Index Table)

= 0.04 / 1.24 = 0.03


8

Because the CR value <0.1, the results are

concluded to be consistent and accepTable. From the

AHP calculation above we get the results from the

value of preference weights (W) or criteria weights.

Where the value of the number of rows for each

element is divided by the number of matrix sizes.

Table 9 Weighting Matrix of All Normalized Criteria Criteria C1 C2 C3 C4 C5 C6 No.

Rows

Weight

Value

C1

C2

C3

C4

C5

C6

0.25

0.50

0.08

0.06

0.05

0.05

0.18

0.35

0.12

0.12

0.12

0.12

0.37

0.37

0.12

0.06

0.04

0.04

0.35

0.26

0.17

0.09

0.04

0.09

0.31

0.19

0.19

0.13

0.06

0.13

0.37

0.22

0.22

0.07

0.04

0.07

1.83

1.89

0.91

0.53

0.35

0.49

0.30

0.32

0.15

0.09

0.06

0.08

Total 1 1 1 1 1 1 6 1

Calculate the eigenvector of each paired comparison

matrix. The eigenvector value is the weight of each

element. This step is to synthesize options in

prioritizing elements at the lowest level of the hierarchy

until the achievement of goals. Synthesis of Priority is

carried out using the eigenvector method to obtain

relative weights for decision-making elements. The

results of weight calculations are used in research

modeling decision support systems for selecting

customers who are eligible for loan funds. So to get the

weight value of the criteria used the AHP method,

which applies the concept of a pairwise comparison

matrix with a comparison value based on the Saaty

index value. Creating a pairwise comparison matrix,

defined criteria will be weighted and compared in pairs

in the form of a matrix.

3.2. Calculation Analysis of TOPSIS Method

Analysis of calculations with the SPK TOPSIS

method is a calculation analysis to find the value of the

solution then obtained an alternative ranking. The role

of the TOPSIS method is to determine alternative

ranking. In the TOPSIS method, the weighted

importance of the values that become criteria is the

result of the Eigen (priority) obtained from the weight

calculation in the AHP method. Following is a weight

table of the criteria along with the cost/benefit value:

Table 10 Cost/Benefits Matrix and Determination Criteria Weight

(w)

Cost/Benefit

C1

C2

C3

C4

C5

C6

0.30

0.32

0.15

0.09

0.06

0.08

Benefit

Benefit

Benefit

Benefit

Benefit

Benefit

The determination of the ranking of matches for

each alternative and each criterion from 1 to 5 is shown

in Table 11 below:

Table 11 Match Value Ranking for Each Alternative and

Every criterion Value Description

1

2

3

4

5

Very Inadequate

Not feasible

Decent enough

Worthy

Very decent

Next make a decision matrix, seen in Table 12.

Table 12 Value weighting interests of each Prospective

Customer Criteria

Alternative C1 C2 C3 C4 C5 C6

Customer 1

Customer 2

Customer 3

Customer 4

Customer 5

Customer 6

Customer 7

Customer 8

Customer 9

Customer 10

Customer 11

Customer 12

Customer 13

5

4

2

5

4

5

3

2

5

4

4

3

1

3

4

2

4

1

2

3

5

2

4

2

3

5

4

5

1

4

5

3

4

1

4

2

4

3

5

2

2

4

1

4

4

2

4

5

3

3

4

4

4

3

3

3

5

4

4

3

2

4

2

3

1

5

2

3

2

4

3

5

5

4

1

5

4

1

Next create a normalized decision matrix R to

reduce the data interval, so that the implementation of

the TOPSIS method is easy and saves memory use.

Calculations using alternative values of one criterion

divided by the square root of the sum of each

alternative per criterion.

Table 13 Normalized Decision Matrix

Alternative

C1 C2 C3 C4 C5 C6

Customer 1

Customer 2 Customer 3

Customer 4


Customer 7

Customer 8

Customer 9

Customer 10


Customer 13

0.36

0.29 0.14 0.36

0.29

0.36

0.21

0.14

0.36

0.29

0.29 0.21

0.07

0.25

0.33 0.16

0.33

0.08 0.16

0.25

0.41

0.16

0.33

0.16 0.25

0.41

0.29

0.37 0.07

0.29

0.37 0.22

0.29

0.07

0.29

0.14

0.29 0.22

0.37

0.16

0.16 0.32

0.08

0.32 0.32

0.16

0.32

0.40

0.24

0.24 0.32

0.32

0.33

0.25 0.25

0.25

0.41 0.33

0.33

0.25

0.16

0.33

0.16 0.25

0.08

0.37

0.15 0.22

0.15

0.30 0.22

0.37

0.37

0.30

0.07

0.37 0.30

0.07

Next determine the normalized decision matrix

weighted Y, with the formula: Vij = WJ * rij. The results

of the normalized decision matrix calculation are

weighted in the following Table 14.

Table 14 Weighted Normalized Decision Matrix Alternative C1 C2 C3 C4 C5 C6


Customer 3


Customer 6


Customer 9


Customer 12

Customer 13

0.11 0.09

0.04 0.11 0.09

0.11

0.06 0.04

0.11

0.09 0.09

0.06

0.02

0.08 0.11

0.05

0.11 0.03

0.05

0.08 0.13

0.05

0.11 0.05

0.08

0.13

0.04 0.06

0.01

0.04 0.06

0.03

0.04 0.01

0.04

0.02 0.04

0.03

0.06

0.01 0.01

0.03

0.01 0.03

0.03

0.01 0.03

0.04

0.02 0.02

0.03

0.03

0.02 0.02

0.02

0.02 0.02

0.02

0.02 0.02

0.01

0.02 0.01

0.02

0.01

0.03 0.01

0.02

0.01 0.02

0.02

0.03 0.03

0.02

0.01 0.03

0.02

0.01


9

Next determine the value of positive and negative

ideal solutions, based on equations 8 and 9. To get the

values in Table 16 calculated using equations 10 and

11. Next, determine the value of the proximity of each

alternative to the ideal solution using equation 12.

Table 15 Results of the Positive (A +) and Negative (A -)

Solution

Alternative C1 C2 C3 C4 C5 C6

Customer 1

Customer 2

Customer 3

Customer 4

Customer 5

Customer 6

Customer 7

Customer 8

Customer 9

Customer 10

Customer 11

Customer 12

Customer 13

0.11

0.09

0.04 0.11

0.09

0.11

0.06

0.04

0.11

0.09

0.09

0.06

0.02

0.08

0.11

0.05

0.11

0.03

0.05

0.08

0.13

0.05

0.11

0.05

0.08

0.13

0.04

0.06

0.01

0.04

0.06

0.03

0.04

0.01

0.04

0.02

0.04

0.03

0.06

0.01

0.01

0.03

0.01

0.03

0.03

0.01

0.03

0.04

0.02

0.02

0.03

0.03

0.02

0.02

0.02

0.02

0.02

0.02

0.02

0.02

0.01

0.02

0.01

0.02

0.01

0.03

0.01

0.02

0.01

0.02

0.02

0.03

0.03

0.02

0.01

0.03

0.02

0.01

(A+) 0.11 0.13 0.06 0.04 0.02 0.03

(A-) 0.02 0.03 0.01 0.01 0.01 0,01

Table 16 Results Distance between weighted values of

each alternative to the ideal positive solution (D+) and

Negative (D-) D+ D-

Customer 1 (D1+)

Customer 2 (D2+)

Customer 3 (D3+)

Customer 4 (D4+)

Customer 5 (D5+)

Customer 6 (D6+)

Customer 7 (D7+)

Customer 8 (D8+)

Customer 9 (D9+)

Customer 10 (D10+)

Customer 11 (D11+)

Customer 12 (D12+)

Customer 13 (D13+)

0.0574

0.0447 0.1144

0.0447

0.1081 0.0842

0.0728

0.0806 0.0824

0.0556

0.0860 0.0728

0.0927

Customer 1 (D1-)

Customer 2 (D2-)

Customer 3 (D3-)

Customer 4 (D4-)

Customer 5 (D5-)

Customer 6 (D6-)

Customer 7 (D7-)

Customer 8 (D8-)

Customer 9 (D9-)

Customer 10 (D10-)

Customer 11 (D11-)

Customer 12 (D12-)

Customer 13 (D13-)

0.1118

0.1140 0.0424

0.1232

0.3287 0.0979

0.0818

0.1126 0.1024

0.1063

0.0830 0.0781

0.1166

Table 17 Ranking of TOPSIS decisions Alternative Score Information

Customer 5 (V5+)

Customer 4 (V4+)

Customer 2 (V2+)

Customer 1 (V1+)

Customer 10 (V10+)

Customer 8 (V8+)

Customer 13 (V13+)

Customer 9 (V9+)

Customer 6 (V6+)

Customer 7 (V7+)

Customer 12 (V12+)

Customer 11 (V11+)

Customer 3 (V3+)

0.7525

0.7338

0.7183

0.6608

0.6566

0.5828

0.5571

0.5541

0.5376

0.5291

0.5176

0.4911

0.2704

Very decent

Very decent

Very decent

Worthy

Worthy

Not feasible

Not feasible

Not feasible

Not feasible

Not feasible

Not feasible

Not feasible

Not feasible

Table 18 Feasibility Value Measurement Tables

Appropriateness Information

>= 0.7000 Very decent

0.6000-0.6999 Worthy

<= 0.5999 Not feasible

CONCLUSION

This research was created to assist the cooperative

leadership in determining the feasibility of applying

for a loan of funds, where measurements are not only

taken from customer data but are taken and considered

from many factors. Resolution of these problems

using two methods for determining the eligibility of

customers who apply for loans, namely the AHP

method to determine the weight value that will be used

to determine the initial input in the TOPSIS method

and use the TOPSIS method for ranking alternatives

so that they can make decisions more effectively,

efficiently and right as a recommendation for the

cooperative.

The combination of the AHP-TOPSIS method was

successfully applied to the SPK determining whether or

not the customer made a loan application and could be

applied by examining various objects but must

theoretically understand the AHP and TOPSIS method

algorithms. From the calculation of the pairwise

comparison matrix, the value of CR = 0.03 shows that

the weight obtained is acceptable and consistent, with

the criteria: business ownership status, ability to repay

loans, character, collateral, income, and customer

salary. The ranking results use the TOPSIS method

after being sorted where the highest value is Customer

5 = 0.7525 (Very Eligible to get a loan of funds) and

the lowest value is Customer 3 = 0.2704 (Not Eligible).

ACKNOWLEDGMENTS

Thanks to the information system, STMIK Triguna

Dharma for support in conducting this research.

REFERENCES

Ahmadi, Sarjon. D dan Jufriadif. N, (2018). Identifikasi

Anggota dalam Penempatan pada Struktur

Organisasi menggunakan Metode Profile

Matching. Jurnal Resti, 2(2 SPK), pp. 452–457.

https://doi.org/10.29207/resti.v2i2.358

Bhima. A. T. H. N, Rekyan. R. M. P. and Nurul. H.

(2018), “Rekomendasi Pemilihan Paket Personal

Computer Menggunakan Metode AHP-TOPSIS,”

J. Pengemb. Teknol. Inf. dan Ilmu Komput., vol.

2, no. 5, pp. 1998–2007, 2018. http://j-

ptiik.ub.ac.id/index.php/j-ptiik/ article/view/1488

Budiharjo, Windarto A. P. dan A. Muhammad (2017),

“Comparison of Weighted Sum Model and Multi

Attribute Decision Making Weighted Product

Methods in Selecting the Best Elementary School

in Indonesia”, International Journal of Software

Engineering and Its Applications, Vol. 11, No. 4,


10

pp. 69-90.

http://dx.doi.org/10.14257/ijseia.2017.11.4.06

Christine. D dan Yeremia. Y (2018),“ Sistem Penyeleksi

Penerima Bantuan Beras Miskin Kauman Kidul

Menggunakan Metode Weighted Product

Berbasis Mobile”, Jurnal Teknik Informatika dan

Sistem Informasi, Vol. 4, No. 1, hal. 103-112.

http://dx.doi.org/ 10.28932/jutisi.v4i1.752

Dwi. N. P. and Rostika. L., (2017). Kaji Banding Metode

TOPSIS, SAW Dan AHP-TOPSIS Guna

Menentukan UKT Mahasiswa Baru Di Politeknik

Negeri Cilacap. Jurnal Infotekmesin, 8(1).

Fifin. S. (2017), “Pemanfaatan Metode Analitycal

Hierarchy Process ( AHP ) Dalam Studi

Kelayakan Sarana Teknis Kereta Api Pada PT .

Kereta Api Indonesia Divisi Regional I Sumatera

Utara Medan,” J. Pembang. Perkota., vol. 5, pp.

59–67, 2017.

http://ejpp.balitbang.pemkomedan.go.id/index.ph

p/JPP/article/view/11

Firdaus. R, Muhammad. T. F, and Nurudin. S. (2018),

“Sistem Pendukung Keputusan Penentuan

Prioritas Perbaikan Jalan Menggunakan Metode

AHP-TOPSIS (Studi Kasus : Dinas Pekerjaan

Umum dan Penataan Ruang Kabupaten

Ponorogo),” J. Pengemb. Teknol. Inf. dan Ilmu

Komput., vol. 2, no. 11, pp. 4365–4370, 2018.

http://jptiik.ub.ac.id/index.php/jptiik/article/view/

2909

Frieyadie. F and Surya. M. R. (2018), “Penerapan Metode

AHP Untuk Membantu Siswa Memilih Jurusan

Yang Tepat Di SMK,” J. RESTI (Rekayasa Sist. dan

Teknol. Informasi), vol. 2, no. 3, pp. 662–667, 2018.

https://doi.org/10.29207/resti.v2i3.396.

Harli. E, (2016). “Pemilihan Network Monitoring System

Berdasarkan Kajian Efektifitas Sistem Informasi

dengan Pendekatan AHP: Studi Kasus pada

―PT.TUV‖”. Jurnal Edukasi dan Penelitian

Informatika (JEPIN). Vol. 2, 2016: 64-70., DOI:

10.26418/jp.v2i1.15555

Hasanah, R. (2016). Decision Support System Validation

Recipient Program Keluarga Harapan in Wonosari

District Using AHP-TOPSIS Method. Jurnal Ilmiah

Teknologi Dan Informasi, 5(2), 111–121.

https://doi.org/10.20961/its.v5i2.1898.

Hertyana. H. (2018), “Sistem Pendukung Keputusan

Penentuan Karyawan Terbaik Menggunakan

Metode TOPSIS,” J. Ilmu Pengetahuan dan

Teknologi Komputer, vol. 4, no. 1, pp. 43–48, 2018.

https://ejournal.nusamandiri.ac.id/index.php/jitk/art

icle/view/317

Jiahui. W, et al. (2019), “Comprehensive evaluation of

voltage stability based on EW-AHP and Fuzzy-

TOPSIS,” Heliyon, vol. 5, no. 10, p. e02410, 2019,

http://dx.doi.org/10.1016/j.heliyon.2019.e02410

Jing. Z, Yaoqi. D., and Xiaojuan. L. (2019), “Study on the

policy of replacing coal-fired boilers with gas-fired

boilers for central heating based on the 3E system

and the TOPSIS method: A case in Tianjin, China,”

Energy, vol. 189, p. 116206, 2019.

https://doi.org/10.1016/j.energy.2019.116206

Karmila. Y., Tursina, Muhammad. A.I, (2019). “ Sistem

Pendukung Keputusan Pemilihan Wilayah

Prioritas Intervensi Kegiatan Keluarga

Berencana.

http://dx.doi.org/10.26418/jp.v5i1.31338

Ma. H, Li. S, and Chan. C.S. (2018), “Analytic Hierarchy

Process (AHP)-based assessment of the value of

non-World Heritage Tulou: A case study of

Pinghe County, Fujian Province,” Tour. Manag.

Perspect., vol. 26, no. August 2017, pp. 67–77,

2018. https://doi.org/10.1016/j.tmp.2018.01.001,

https://www.sciencedirect.com/science/article/ab

s/pii/S2211973618300011

Mahmud. S., Tursina, and Yulianti. (2019), “Sistem

Pendukung Keputusan Pemilihan Daerah

Prioritas Penanganan Stunting pada Balita

Menggunakan Metode TOPSIS (Studi Kasus :

Kota Pontianak),” J. Sist. dan Teknol. Inf., vol. 7,

no. 1, p. 33, 2019.

http://dx.doi.org/10.26418/justin.v7i1.27815

Mubarok. A, et al. (2019), “Sistem Pendukung Keputusan

Kelayakan Pemberian Kredit Dengan Metode

TOPSIS,” J. Inform., vol. 6, no. 1, pp. 37–46,

2019., DOI: 10.31311/ji.v6i1.4739

Munawir, M. (2020) Analisis Penilaian Kinerja

Terhadap Rasionalitas Dosen pada 114

Perguruan Tinggi Swasta ( PTS ) Wilayah

Kopertis XIII Provinsi Aceh dengan Metode

Technique for Order Preference by Similarity to

Ideal Solution ( TOPSIS ). (April).

https://doi.org/10.35870/jtik.v2i1.45

Nikolas. P, et al. (2019), “Spatial analysis and evaluation

of a coal deposit by coupling AHP & GIS

techniques,” Int. J. Min. Sci. Technol., no. xxxx,

2019, https://doi.org/10.1016/j.ijmst.2019.04.002

Nofriansyah. D, (2014), Konsep Data Mining Vs Sistem

Pendukung Keputusan.pdf, Ed.1, Cet.

Yogyakarta: Deepublish, 2014.

Oktopanda. (2017), “A Study Approach of Decission

Support System with Profile Matching”,

International Journal of Recent Trends in

Engineering & Research (IJRTER), Vol. 03, No.

02. ISSN: 2455-1457.

http://dx.doi.org/10.23883/IJRTER.2017.3001.Z

L93N

Pornwasin. S and Tossapol. K. (2018), “Fuzzy AHP-

TOPSIS approaches to prioritizing solutions for

reverse logistics barriers,” Comput. Ind. Eng., vol.

117, pp. 303–318, 2018.

https://doi.org/10.1016/j.cie.2018.01.015

Rahayu, T., Krisnanik, E., & Hananto, B. (2019),

“Metode Analytical Hierarchy Process Dalam

Menentukan Pemilihan Desa Terbaik,” J. RESTI

(Rekayasa Sist. dan Teknol. Informasi), vol. 3, no.

1, pp. 94–99, 2019.

DOI: https://doi.org/10.29207/resti.v3i1.769

Rizqi, M., Akbar, A., Fitrian, Y., & Maseleno, A, (2018),

“Dismissal Working Relationship using Analytic

Hierarchy Process Method,” Int. J. Pure Appl.

Math., vol. 118, no. 7 Special Issue, pp. 177–184,

2018.

Sampreet. K. R. et al. (2019), “Optimization of parameters

in Nd: YAG laser welding of Ti-6Al-4V using

TOPSIS,” Mater. Today Proc., pp. 4–7, 2019.,

https://doi.org/10.1016/j.matpr.2019.05.401


11

Santika, P. P., & Handika, I. P. S. (2019), “Sistem

Pendukung Keputusan Penerimaan Karyawan

Dengan Metode AHP Topsis (Studi Kasus: PT.

Global Retailindo Pratama),” Sintech, vol. 2, no. 1,

pp. 1–9, 2019.

https://doi.org/10.31598/sintechjournal.v2i1.321

Sindhu. R. (2018), “Sistem Pendukung Keputusan Dalam

Menentukan Penilaian Kinerja Guru Dengan

Metode Analytical Hierarchy Process (AHP),”

Simetris J. Tek. Mesin, Elektro dan Ilmu Komput.,

vol. 9, no. 2, pp. 1001–1008, 2018.

DOI: https://doi.org/10.24176/simet.v9i2.2528,

Suthep. B and Puntiva. P. (2019), “Uncertain risk

assessment modelling for bus body manufacturing

supply chain using AHP and fuzzy AHP,” Procedia

Manuf., vol. 30, pp. 663–670, 2019,

https://doi.org/10.1016/j.promfg.2019.02.094

Tayfun. A. (2017), “Specifying the Determinants of Brand

Value by Using AHP Method,” International

Journal of Academic Research in Accounting,

Finance and Management Sciences Vol. 7, No.1, pp.

98–106, 2017,

http://dx.doi.org/10.6007/IJARAFMS/v7-i1/2574

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